assessing changeability in aerospace systems architecting...

© 2006 Adam Ross and Daniel Hastings/09.20.06- 1 lean.mit.edu

Assessing Changeability in Aerospace Systems Architecting and Design Using Dynamic Multi-

Attribute Tradespace Exploration (AIAA 2006-7255)

Adam Ross, MIT Postdoctoral Associate

and Daniel Hastings, MIT Professor AIAA Space 2006, Session 52-SPS-4

September 20, 2006


Presentation Overview

1. Introduction 2. Defining Changeability 3. Quantifying Changeability 4. Case Applications

1. Hypothetical Low Earth Orbit Satellite 2. Currently Deployed Weapon System 3. Proposed Large Astronomical Observatory

5. Discussion 6. Conclusion


Meeting Customer Needs

• Goal of design is to create value (profits, usefulness, voice of the customer, etc…)

• Requirements capture a mapping of needs to specifications to guide design


Deploying a “Valuable” System…



Contexts change…


Meeting Customer Needs (cont.)







• People change their minds… • To continue to deliver value, systems must

change as well…


Defining Changeability

• What is change? • Defined by differences… • Necessity of time…

• Three parts • Change agent (“who” caused change, force instigator) • Change mechanism (how changed, including “cost”) • Change effect (what changed, “State 2 – State 1”)

• All things change, but some things are more “changeable” than others

State 2 State 1 “Cost” for change

Change

agent


The Elements of Change

Change Agent

Change Effect

Change Mechanism



Change Agent

Change Effect

Change Mechanism



Change Agent

Change Effect

Change Mechanism

Change elements can be used to classify the change type…


Flexibility vs. Adaptability: Change Agent Origin

Change Agent

Change Effect

Change Mechanism

Flexible Change-type

(Outside System)

Adaptable Change-type (Inside System)


How to Change: Change Mechanism

Will revisit in a few slides

Change Agent

Change Effect

Change Mechanism


Robustness, Scalability, Modifiability: Change Effect

Robust (No change)

Scaleable (Parameter level)

Modifiable (Parameter set)

Change Agent

Change Effect

Change Mechanism


Change Agents and Effects

Change Agent

Internal(Adaptable)

External(Flexible)

None(Rigid)

Change Agent

Internal(Adaptable)

External(Flexible)

None(Rigid)

Change Effect

Parameter level(Scalable)

Parameter set(Modifiable)

None(Robust)

Change Effect



None(Robust)

Change Effect



None(Robust)

Changeability

Change agent origin

Change effect type

+

Change agents and effects are used to classify the change type… what about change mechanisms?


Change Mechanism

Change requires a mechanism to link beginning and end states

Cost?

? 1 2


Change Mechanism

Change mechanisms specify paths between states Many paths may link the same two states

1 2

3 4

Cost

1 2


Change Summarized

Change Agent

Internal(Adaptable)

External(Flexible)

None(Rigid)

Change Agent

Internal(Adaptable)

External(Flexible)

None(Rigid)

Change Effect



None(Robust)

Change Effect



None(Robust)

Change Effect



None(Robust)

Changeability

Now that changeability is defined… how can it be used to evaluate systems?

1 2

3 4

Cost

1 2

Change type

Ways to change

+ Change Mechanism


Tradespaces Defined

Total Lifecycle Cost ($M2002)

Assessment of cost and utility of large space of possible system designs

ATTRIBUTES: Design decision metrics – Data Lifespan (yrs) – Equatorial Time (hrs/day) – Latency (hrs) – Latitude Diversity (deg) – Sample Altitude (km)

Orbital Parameters – Apogee Altitude (km) – Perigee Altitude (km) – Orbit Inclination (deg)

Spacecraft Parameters – Antenna Gain – Communication Architecture – Propulsion Type – Power Type – Total Delta V

DESIGN VARIABLES: Design trade parameters

Each point is a specific design

Attributes Utility

Design Variables “Cost”

Analysis

Tradespace: {Design,Attributes} {Cost,Utility}

Value

Concept

Firm Designer Customer

User

Value-driven design…


Mapping Reality to Perception

RK(DViDVj)

{XM}

U({XM}){XM}| Utility

0 1

Utility0 1

Utility

“Perceived” Space Robustness, Scalability,

Modifiability

“Simulation”

FXM({DVN})

{DVN}|{XM}“Simulation”

FXM({DVN})

{DVN}|{XM}

{DVN}

C({DVN}){DVN} | Cost

“Real” Space

Rig

idity

, Fle

xibi

lity,

Ad

apta

bilit

y

0 Inf

Cost0 Inf

Cost

Cost

Utility(t)

(t)

(t)

(t)

Designer “control” resides

here

Decision maker “control” resides

here


Tradespace Networks

Cost

Utility

Cost

Utility

Transition rules

Transition rules are mechanisms to change one design into another The more outgoing arcs, the more potential change mechanisms

Tradespace designs = nodes

Applied transition rules = arcs

12

34

Cost

1 2

12

34

Cost

1 2


Determining Changeability

The Question: Is the system _____________? (Flexible, Adaptable, Robust,

Scalable, Modifiable, Changeable, Rigid, etc…)

The Answer: It depends!

The question of changeability is partly subjective: Is the “cost” for change acceptable?

100 101 102 103 104 105 106 …

Yes No

Cost or Time


objective subjective

Changeability Metric: Filtered Outdegree

Filtered Outdegree# outgoing arcs from design at acceptable “cost”

(measure of changeability)

Subjective FilterOutdegree

Cost

Filtered Outdegree# outgoing arcs from design at acceptable “cost”

(measure of changeability)

Subjective FilterOutdegree

Cost

Outdegree # outgoing arcs

from a given node

ODK

RK

RK+1

RK+1

ODK

RK

RK+1

RK+1

ODK

RK

RK+1

RK+1

Filtered outdegree is a measure of the apparent changeability of a design


Case Application: X-TOS

• Goal: Determine atmospheric density over range of altitudes and latitudes

• Low Earth orbiting satellite with in-situ sampling payload

• Customer: Payload scientist at AFRL/Hanscom • Mission analyzed and system designed by MIT

graduate space system design course in Spring 2002 Number of Designs Explored: 50488


X-TOS RealPerceived

Design Parameters Attributes Inclination Inc Current Data Lifespan DL Current Apogee Altitude Aa Current Latitude Diversity LD Current Perigee Altitude Ap Current Equator Time ET Current Communication Architecture CA Current Latency L Current Total Delta-V ∆V Current Sample Altitude SA Current Propulsion Type PpT Current Power Type PwT Current Antenna Gain AG Current

Designer defined Decision Maker defined

Pareto Set (highest utility at given cost) = “best” designs

Static analysis reveals set of value efficient designs (“Best” designs at a given lifecycle cost)

Model/ Simulation


X-TOS Tradespace Network

Rule Description Change agent origin

R1: Plane Change Increase/decrease inclination, decrease ∆V Internal (Adaptable)

R2: Apogee Burn Increase/decrease apogee, decrease ∆V Internal (Adaptable)

R3: Perigee Burn Increase/decrease perigee, decrease ∆V Internal (Adaptable)

R4: Plane Tug Increase/decrease inclination, requires “tugable” External (Flexible)

R5: Apogee Tug Increase/decrease apogee, requires “tugable” External (Flexible)

R6: Perigee Tug Increase/decrease perigee, requires “tugable” External (Flexible)

R7: Space Refuel Increase ∆V, requires “refuelable” External (Flexible)

R8: Add Sat Change all orbit, ∆V External (Flexible)

Rule-Effects Matrix

X-TOS Inc Aa Ap CA ∆V PpT PwT AG Rf Tg Up

DV1DV2DV3DV4DV5DV6DV7DV8IV1 IV2 IV3 Flx Adp

Rule Plane change R1

Apogee burn R2

Perigee burn R3

Plane tug R4

Apogee tug R5

Perigee tug R6

Space refuel R7

Add sat R8

Origin

Design Variables Path Enablers Change

Rules are used to specify transition paths between any two designs using automated algorithms

Proposed X-TOS Rules Rules are generated and evaluated in terms of effects on design parameters

Accessibility matrices specify links between design i and j

ODK

RK

RK+1

RK+1

ODK

RK

RK+1

RK+1

ODK

RK

RK+1

RK+1


Outdegree results

Pareto Set designs (903, 1687, 2535, 2471) are not the most changeable

Design 7156 becomes relatively more changeable as cost threshold increases

4.21

150

2.27

6

140

0.51

Low

Fuel Cell

Chem

1200

TDRSS

150

460

70

1687

4.21

150

2.27

11

60

0.51

Low

Fuel Cell

Chem

1200

TDRSS

150

460

30

903

4.21

150

2.27

5

180

0.51

Low

Fuel Cell

Chem

1200

TDRSS

150

460

90

2471

4.154.994.524.88Cost ($10M)

350150150290Sample Alt

2.402.672.422.30Latency

5225Eq Time

180180140180Lat Div

110.610.5210.05Data Life

LowLowLowLowAnt Gain

Solar ArraySolar ArrayFuel CellFuel CellPwr Type

ChemElecElecChemProp Type

1000120012001200Delta V

TDRSSTDRSSTDRSSTDRSSCom Arch

350150150290Perigee

77020001075460Apogee

90907090Inclination

7156303019092535DV

4.21

150

2.27

6

140

0.51

Low

Fuel Cell

Chem

1200

TDRSS

150

460

70

1687

4.21

150

2.27

11

60

0.51

Low

Fuel Cell

Chem

1200

TDRSS

150

460

30

903

4.21

150

2.27

5

180

0.51

Low

Fuel Cell

Chem

1200

TDRSS

150

460

90

2471

4.154.994.524.88Cost ($10M)


2.402.672.422.30Latency

5225Eq Time

180180140180Lat Div

110.610.5210.05Data Life




1000120012001200Delta V


350150150290Perigee

77020001075460Apogee

90907090Inclination

7156303019092535DV


Outdegree results

Pareto Set designs (903, 1687, 2535, 2471) are not the most changeable

Design 7156 becomes relatively more changeable as cost threshold increases

4.21

150

2.27

6

140

0.51

Low

Fuel Cell

Chem

1200

TDRSS

150

460

70

1687

4.21

150

2.27

11

60

0.51

Low

Fuel Cell

Chem

1200

TDRSS

150

460

30

903

4.21

150

2.27

5

180

0.51

Low

Fuel Cell

Chem

1200

TDRSS

150

460

90

2471

4.154.994.524.88Cost ($10M)


2.402.672.422.30Latency

5225Eq Time

180180140180Lat Div

110.610.5210.05Data Life




1000120012001200Delta V


350150150290Perigee

77020001075460Apogee

90907090Inclination

7156303019092535DV

4.21

150

2.27

6

140

0.51

Low

Fuel Cell

Chem

1200

TDRSS

150

460

70

1687

4.21

150

2.27

11

60

0.51

Low

Fuel Cell

Chem

1200

TDRSS

150

460

30

903

4.21

150

2.27

5

180

0.51

Low

Fuel Cell

Chem

1200

TDRSS

150

460

90

2471

4.154.994.524.88Cost ($10M)


2.402.672.422.30Latency

5225Eq Time

180180140180Lat Div

110.610.5210.05Data Life




1000120012001200Delta V


350150150290Perigee

77020001075460Apogee

90907090Inclination

7156303019092535DV

Outdegree functions reveal differential nature of apparent changeability


Outdegree Tradespaces

For this plot, Ĉ=C∞

More changeable(ie including flexible, adaptable, scalable

and modifiable)

Colored by outdegree

Outdegree-colored tradespace shows

dominated designs with superior changeability

Apparent changeability increases differentially

across a tradespace with increasing acceptable cost

threshold

Tradespace changeability analysis allows focus on more changeable system designs

↑Ĉ


Other Case Studies

Joint Direct Attack Munition (JDAM)

Terrestrial Planet Finder (TPF)

• System highly flexible • Potential variants may readily address future needs

• Science expectations may be diverging with time • Should seek reconfigurability, dynamic scheduling

Please see paper (and thesis) for more details


Discussion

• Computational requirements • Effort for static and dynamic modeling • Focus on Design for Changeability

• Increase number of paths (change mechanism) • Lower “cost” or increase acceptability threshold

(apparent changeability) • Mindshift: recognize dynamic context and fallacy of

static preferences

• Concept-independent measure of changeability


Conclusion

• Change includes three elements • Change agent • Change mechanism • Change effect

• Change taxonomy links agents and effects • Change mechanism drives filtered outdegree • Quantifiable filtered outdegree couples both objective

and subjective measures • Changeability can be used as an explicit and

consistent metric for designing systems

Designed for changeability, systems will be empowered to become value robust, delivering value in spite of context

and preference changes


Thank you for your attention!

Any questions?

For further details on topic please see: Ross, Adam M., Managing Unarticulated Value: Changeability in

Multi-Attribute Tradespace Exploration. Cambridge, MA: MIT. PhD in Engineering Systems. 2006.


Back-up Slides…


value

Architecture Exploration

Architecture Evaluation

Need Identification

Design Exploration

Design Evaluation

System Design(s)



Need Identification

Design Exploration

Design Evaluation

System Design(s)



Need Identification

Design Exploration

Design Evaluation

System Design(s)

MATE is… • A decision maker preference-directed

tradespace exploration process

• A formalized framework and flexible philosophy

• Robust to changes and concepts

• Extended by 8 MIT theses (2002-2004)

Tradespace Solution Spaces

Dual-SM Aero/Astro, TPP 2003

3. Static Value-centric Design: Multi-Attribute Tradespace Exploration

Lifecycle Cost ($M)

Util

ity


Attribute: A decision maker-perceived metric that measures how well a decision maker-defined objective is met

In practice, the “Rule of 7” applies: Human mind limited to roughly 7 (7 ± 2) simultaneous concepts*

*Miller, G. A. "The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information." The Psychological Review 63 (1956): 81-97.

In the limit ranges converge to a point, the attributes become requirements

Attribute Characteristics

• Definition

• Units

• Range (least-most acceptable)

3.1 Articulated Value: Attributes as Decision Metrics

Attributei ≡ Xi


3.2 Quantified Concepts: Design Variables as Tradable Parameters

# S/C per Cluster

…. # Clusters

….

Aperture Diameter

….

Constellation Altitude

Antenna Power

1 2 3 4 5 6 7 8 9 100

10

20

30

40

50

60

70

80

90

100

Design Vector No mothership / Mothership Swarm Orbit Parameters

Number of spacecraft in swarm

Geometry of swarm

Design Variable: A designer-controlled quantitative parameter that reflects an aspect of a concept

Design Variablei ≡ DVi


6.2 Types of Changes: Scalability

Change in attribute level

“Scalable”

Now (state 1)

Later (state 2)

U

XX1 X2

U1

U2

U

XX1 X2

U1

U2

A box can be quantified in terms of scalable in Xi

(i.e., can Xi be changed from Xi1 to Xi

2?)


6.2 Types of Changes: Modifiability

Change in attribute set

“Modifiable”

U=f(X1,X2,X3,X4)

U’=f(X1,X2,X3,X4,X5)

Now (state 1)

Later (state 2)

A box can be quantified in terms of modifiable in Xi

(i.e., can Xi be added to or deleted from the attribute set?)

X1 X2

X3

X4

X5

X1 X2

X3

X4

X5


6.2 Types of Change: Robustness

No change in perceived value

“Robust” A box can be quantified in terms of

robust in Xi to “Input” change (i.e., can Xi remain “constant” over

range of “Input”?)

Xgoal

∆Inputs

“Constant”

Since the level of attribute performance is a function of the inputs (and constraints including environment), robustness is an insensitivity to the

inputs (and constraints)

X

Inputs

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